The success of the model responding to you with a correct information is a function of giving it proper context too.

That hasn't changed nor I think it will, even with the models having very large context windows (eg Gemini has 2M). It is observed that having a large context alone is not enough and that it is better to give the model sufficiently enough and quality information rather than filling it with virtually everything. Latter is also impossible and does not scale well with long and complicated tasks where reaching the context limit is inevitable. In that case you need to have the RAG which will be smart enough to extract the sufficient information from previous answers/context, and make it part of the new context, which in turn will make it possible for the model to keep its performance at satisfactory level.